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The increasing prominence of digital tools for city-scale solar analysis emphasizes the need for validation methodologies, which include urban environmental monitoring and data quality control. This study addresses this gap by introducing a quality control scheme for solar irradiance measurements, using a typical street canyon in Geneva (Switzerland) as a case study. The developed quality control scheme is replicable and effectively addresses challenges posed by the built environment, distinguishing it from existing methods that mostly apply to measurements from unobstructed sensors. The experimental data used in this study were retrieved from the monitoring system installed on two opposing facades of the street canyon case study, as well as a nearby weather station. Measurements were recorded from December 22nd, 2022, to December 19th, 2023, at a one-minute time resolution. Five quality checks – nighttime check, range limit tests, precipitation check, shadow detection, and consistency check - were defined to identify the potential flaws and disturbances in the dataset so that these data points could be flagged accordingly. The results consist of reliable solar irradiance data over one year, which can be used in the future for validating a new component for modeling façade solar potential within the Grand Geneva Area Solar Cadaster.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021–17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified. [ABSTRACT FROM AUTHOR]

Urban environmental risks are related to dynamic and long-term cross-processes arising from complex interconnected relationships. Although they have various sources, trace metals' ability to accumulate is relatively high compared to other pollutants. Therefore, for this reason, heavy metals can be found in high amounts in cities, especially in road dust. The targets of the present study are to appoint the levels and sources of trace metals in road dust samples collected from eleven areas in the Düzce city center. Because of their potential health risks, the five heavy metals (Cd, Cr, Cu, Ni, and Pb) are the most commonly studied pollutants. The inhalation of them through the mouth and nose is almost negligible; however, ingestion is a higher potential health risk for children. The hazard index (HI) and geoaccumulation index (Igeo) are powerful tools used to assess the level of risk. Factors governing possible contamination mean values were evaluated in the following order: Pb (56.07 mg/kg) > Cu (43.45 mg/kg) > Ni (30.05 mg/kg) > Cr (26.58 mg/kg) > Cd (4.33 mg/kg). The noncarcinogenic risks of Pb poses are relatively higher than those posed by the other four metals for both children and adults. However, HI values of Cd, Pb, and Ni in children were 1.25–1.61, 2.93–3.74, and 1.00–1.14; Cd is 1.05–2.56. The HI values for children are relatively higher than for adults. This paper provides the most significant contribution to road dust is atmospheric deposition by industrial activities and traffic density. Regarding Pb, while Igeo is 0.66 in park areas and 0.61 in forest areas, it reaches 0.96 on highways. While Ni is calculated for Igeo as 0.52 in forest area, it gets 0.97 in industrial factory surroundings. The findings reveal the multidimensional results of land use policies regarding sustainable urban development. The stochastic model obtained is vital, especially in disadvantaged groups. [ABSTRACT FROM AUTHOR]

Germany aims to reduce soil sealing to under 30 hectares per day by 2030 to address negative environmental impacts from the expansion of impervious surfaces. As cities adapt to climate change, spatially explicit very high-resolution information about the distribution of impervious surfaces is becoming increasingly important for urban planning and decision-making. This study proposes a method for mapping impervious surfaces in Google Earth Engine (GEE) using a data fusion approach of 0.9 m colour-infrared true orthophotos, digital elevation models, and vector data. We conducted a pixel-based random forest (RF) classification utilizing spectral indices, Grey-Level Co-occurrence Matrix texture features, and topographic features. Impervious surfaces were mapped with 0.9 m precision resulting in an Overall Accuracy of 92.31% and Kappa-Coefficient of 84.62%. To address challenges posed by high-resolution imagery, we superimposed the RF classification results with land use data from Germany's Authoritative Real Estate Cadastre Information System (ALKIS). The results show that 25.26% of the city of Wuppertal is covered by impervious surfaces coinciding with a government-funded study from 2020 based on Sentinel-2 Copernicus data that defined a proportion of 25.22% as built-up area. This demonstrates the effectiveness of our method for semi-automated mapping of impervious surfaces in GEE to support urban planning on a local to regional scale. [ABSTRACT FROM AUTHOR]

The identification of colored steel buildings in images is crucial for managing the construction sector, environmental protection, and sustainable urban development. Current deep learning methods for optical remote sensing images often encounter challenges such as confusion between the roof color or shape of regular buildings and colored steel structures. Additionally, common semantic segmentation networks exhibit poor generalization and inadequate boundary regularization when extracting colored steel buildings. To overcome these limitations, we utilized the metal detection and differentiation capabilities inherent in synthetic aperture radar (SAR) data to develop a network that integrates optical and SAR data. This network, employing a triple-input structure, effectively captures the unique features of colored steel buildings. We designed a multimodal hybrid attention module in the network that discerns the varying importance of each data source depending on the context. Additionally, a boundary refinement (BR) module was introduced to extract the boundaries of the colored steel buildings in a more regular manner, and a deep supervision strategy was implemented to improve the performance of the network in the colored steel building extraction task. A BR module and deep supervision strategy were also implemented to sharpen the extraction of building boundaries, thereby enhancing the network’s accuracy and adaptability. The results indicate that, compared to mainstream semantic segmentation, this method effectively enhances the precision of colored steel building detection, achieving an accuracy rate of 83.19%. This improvement marks a significant advancement in monitoring illegal constructions and supporting the sustainable development of the Beijing–Tianjin–Hebei metropolitan region.

Culture, creativity and circularity are driving forces for the transition of cities towards sustainable development models. This contribution proposes a data-driven quantitative methodology to compute cultural performance indices of cities (C4 Index) and thus compare results derived by subjective and objective assessment methods within the case study of the Metropolitan City of Naples. After data processing with Machine-Learning (ML) algorithms, two methods for weighting the indicators were compared: principal component analysis (PCA) and geographically weighted linear combination (WLC) with budget allocation. The results highlight similar trends among higher performance in seaside cities and lower levels in the inner areas, although some divergences between rankings. The proposed methodology was addressed to fill the research gap in comparing results obtained with different aggregation methods, allowing a choice consistent with the decision-making environment.

The research proposal reports the outcomes of a research track concerning the automation of the architectural census of technological elements in urban environments, aiming at the development of a monitoring and management system for the built heritage. The proposal is focused on a set of specific elements (water tanks) stacked on the coverings of the historical centre of Bethlehem and leverages close-range photogrammetric acquisitions to train Deep Learning models. The model lifecycle management, from training to prediction and deployment, as well as the storage of both image data and metadata, is performed through the scalability of a Cloud enterprise architecture. Periodical scheduled monitoring enables comparisons across different periods, allowing the detection of modifications, removals, and additions, therefore identifying the insurgence of potential criticalities. The goal of the project is the definition of a protocol to automate the identification of recurrent elements and monitor their evolution through time.

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The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occur-rences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors' data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices. [ABSTRACT FROM AUTHOR]

Culture, creativity and circularity are driving forces for the transition of cities towards sustainable development models. This contribution proposes a data-driven quantitative methodology to compute cultural performance indices of cities (C4 Index) and thus compare results derived by subjective and objective assessment methods within the case study of the Metropolitan City of Naples. After data processing with Machine-Learning (ML) algorithms, two methods for weighting the indicators were compared: principal component analysis (PCA) and geographically weighted linear combination (WLC) with budget allocation. The results highlight similar trends among higher performance in seaside cities and lower levels in the inner areas, although some divergences between rankings. The proposed methodology was addressed to fill the research gap in comparing results obtained with different aggregation methods, allowing a choice consistent with the decision-making environment. [ABSTRACT FROM AUTHOR]

Urbanization creates new development in open spaces and agricultural fields, synonymous with increasing impervious surfaces. Such surfaces restrain the natural infiltration of water, and directly affect the non-point source pollution. Thus, consequential events like flooding and surface water degradation require spatial and quantitative information on impervious surfaces. Remote sensing technologies are widely used in impervious surface mapping of various geographical locations for environmental monitoring. In this study, the datasets from recently launched European Space Agency satellites (Sentinel-1 and Sentinel-2) and random forest classifier are used. The impervious surface growth of the study area, Lahore city, in 2015 and 2021, and growth trends are assessed. Results are validated with classification accuracy and comparison with similar datasets. The objective is to develop a reliable impervious surface mapping method with land cover quantification technique from multisource datasets. With a chi-square value of greater than 3.84 obtained from the McNemar test, the performance of fused data was superior to that of optical alone data in the classification. Over a 5-year period, Lahore grew at an annual rate of 2.14% comparable to the findings of Copernicus Land Services and the Atlas of Urban Expansion with an underestimation of 1% and 8.75%, respectively. Improvements in overall accuracy (2.7%) and kappa coefficient (5%) were seen in classified maps from fused datasets. Fusion of Sentinel datasets provide a reliable means of impervious surface mapping at city scale as an indicator of environmental quality which is valuable for the sustainable management of the city. [ABSTRACT FROM AUTHOR]

• Quantitative assessment of the heat waves in different city zones. • Quantification of the heat waves impact on building energy performance. • Influence of heat waves on the indoor operative temperature. • Impact of the synergic effect of heat waves and urban heat island. Global and local climate change increases the occurrence and the magnitude of extreme phenomena, as urban heat island and heat waves. These phenomena seriously affect the quality of life in several aspects: society, health, environment; they also heavily affect the building sector, increasing the energy use for cooling and deteriorating the indoor thermal environment. This paper utilizes data from a continuous urban microclimatic monitoring over three years to quantify the impact of heat waves on the thermal quality of two reference residential buildings in the city of Rome, Italy. The synergic effect of heat waves with the urban heat island is also analysed. The observation period includes summers of 2015, 2016 and 2017. The buildings' response is analysed through numerical thermal analyses in transient regime, taking into account several variants: thermal insulation, mechanical cooling system and thermal free-floating conditions, with different night ventilation strategies. Results show that daily average temperature and urban heat island intensity increase by up to 4.3 °C and 1.5 °C respectively during the heat waves with respect to the rest of the summer. The building cooling energy use rises up 87% during heat wave periods, while average operative temperature in free-running buildings increments by up to 3.5 °C. Results also show the impressive combined impact of heat wave and heat island: triplication of cooling energy use in the worst case and increase of the average operative temperature above 5 °C. [ABSTRACT FROM AUTHOR]

Flood control and water resources management require monitoring the water level in rivers and streams. Water level measurement techniques increasingly consider image processing procedures. Most of the systems use a staff gauge to support the waterline detection. However, these techniques can fail when applied to urban stream channels due to water undulation, debris on the water surface, and traces of rain captured by the camera, and other adverse effects on images can be quite dramatic on the results. The importance of considering these effects is that they are usually associated with the variation in the water level with the occurrence of rain. The technique proposed in this work uses a larger detection zone to minimize the effects that tend to obstruct the waterline. The developed system uses an infrared camera to operate during the day and night. Images acquired in different weather conditions helped to evaluate the proposed technique. The water level measurement accuracy was about 1.8 cm for images taken during the day and 2.8 cm for images taken at night. During short periods of heavy rain, the accuracy was 2.6 cm for the daytime and 3.4 cm for the nighttime. Infrared lighting can improve detection accuracy at night. The developed technique provides good accuracy under different weather conditions by combining information from various detection positions to deal with waterline detection issues.

. The Permanent Scatterer Interferometry (PSI) represents one of the most advanced monitoring techniques from space. In the current paper, the technique is applied for observing the movement behaviors of buildings found in the center of Romania’s capital, Bucharest, in order to verify whether there is a possibility to differentiate among patterns. The main hypothesis of the research is that buildings respond to ground movement differently depending on their characteristics, such as age, construction material, and structure or height regime. Twenty-seven images acquired by the German TerraSAR-X (TSX) satellite, were processed in order to depict ground level deformations. The buildings were analyzed by classifying them in different categories, depending on their earthquake vulnerability, height and location. The results suggested that the movement patterns identified by the satellite depend mainly on the spatial distribution of the buildings.

The model of sustainable development of the territory should be in a state of control and management. The system of urban monitoring of Ukraine does not fully provide information on the level of sustainable development of settlements and regions. Therefore, it is necessary to create systems for monitoring indicators of sustainable development of human settlements and regions. The objective of this study is to analyze the existing programs for stimulating sustainable development in European countries and to develop recommendations on the need to organize such systems in Ukraine and to improve the system of urban monitoring. The article describes such systems and programs: URBACT is a program for sharing best practices between cities by creating thematic networks. URBACT’s mission is to encourage cities to work together and develop integrated solutions to common urban problems, through networking, to learn from each other’s experiences and identify best practices in order to improve urban policies; URBAN AUDIT – a large set of statistical information. The main objective of the system is to provide objective and comparable statistical data on European cities; URBAN ATLAS – provides a pan-European comparison of urban land use data. The information is in the form of open geospatial data. The system is aimed at facilitating work on site planning and site accounting. It is necessary to adopt the best practices of implementing sustainable development technology and apply it in other countries that have chosen a model for their development – a model for sustainable development of the territory. The current system of town-planning monitoring in Ukraine needs to be improved and given a new task – to take into account indicators of sustainable development of the territories. This system is most suitable for this task, since urban monitoring already takes into account certain indicators in the form of spatial data.

With over 179,000 employees worldwide, Veolia designs and delivers sustainable and competitive water, waste and energy management solutions to its customers. The Innovation & Markets division is tasked to develop marketing at company level and to steer R&D efforts to accelerate changes in Veolia’s activities and business models.This article presents the Urban Environmental Monitoring demonstration project, developed jointly by the Nice Côte d’Azur metropolitan authority, Veolia, Orange, m2oCity and IBM since 2012. Exploring new ways of combining new technologies and social sciences, the project seeks to exploit a broad range of data to offer new urban services, designed to make the city of tomorrow more attractive, sustainable and competitive.

In the context of rapid urbanization, monitoring the evolution of cities is crucial. To do so, 3D change detection and characterization is of capital importance since, unlike 2D images, 3D data contain vertical information of utmost importance to monitoring city evolution (that occurs along both horizontal and vertical axes). Urban 3D change detection has thus received growing attention, and various methods have been published on the topic. Nevertheless, no quantitative comparison on a public dataset has been reported yet. This study presents an experimental comparison of six methods: three traditional (difference of DSMs, C2C and M3C2), one machine learning with hand-crafted features (a random forest model with a stability feature) and two deep learning (feed-forward and Siamese architectures). In order to compare these methods, we prepared five sub-datasets containing simulated pairs of 3D annotated point clouds with different characteristics: from high to low resolution, with various levels of noise. The methods have been tested on each sub-dataset for binary and multi-class segmentation. For supervised methods, we also assessed the transfer learning capacity and the influence of the training set size. The methods we used provide various kinds of results (2D pixels, 2D patches or 3D points), and each of them is impacted by the resolution of the PCs. However, while the performances of deep learning methods highly depend on the size of the training set, they seem to be less impacted by training on datasets with different characteristics. Oppositely, conventional machine learning methods exhibit stable results, even with smaller training sets, but embed low transfer learning capacities. While the main changes in our datasets were usually identified, there were still numerous instances of false detection, especially in dense urban areas, thereby calling for further development in this field. To assist such developments, we provide a public dataset composed of pairs of point clouds with different qualities together with their change-related annotations. This dataset was built with an original simulation tool which allows one to generate bi-temporal urban point clouds under various conditions.

Sensors can measure air quality, traffic congestion, and other aspects of urban environments. The fine-grained diagnostic information they provide could help urban managers to monitor a city's health. Recently, a “drive-by" paradigm has been proposed in which sensors are deployed on third-party vehicles, enabling wide coverage at low cost. Research on drive-by sensing has mostly focused on sensor engineering, but a key question remains unexplored: How many vehicles would be required to adequately scan a city? Here, we address this question by analyzing the sensing power of a taxi fleet. Taxis, being numerous in cities, are natural hosts for the sensors. Using a ball-in-bin model in tandem with a simple model of taxi movements, we analytically determine the fraction of a city's street network sensed by a fleet of taxis during a day. Our results agree with taxi data obtained from nine major cities and reveal that a remarkably small number of taxis can scan a large number of streets. This finding appears to be universal, indicating its applicability to cities beyond those analyzed here. Moreover, because taxis' motion combines randomness and regularity (passengers' destinations being random, but the routes to them being deterministic), the spreading properties of taxi fleets are unusual; in stark contrast to random walks, the stationary densities of our taxi model obey Zipf's law, consistent with empirical taxi data. Our results have direct utility for town councilors, smart-city designers, and other urban decision makers. [ABSTRACT FROM AUTHOR]

The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller.

The use of InSAR (Interferometric Synthetic Aperture Radar) products has greatly increased in the last years because of the technological advances in terms of both acquisition sensors and processing algorithms. The development of multi-interferogram techniques and the availability of free SAR analysis tools has significantly increased the number of worldwide applications of satellite measurements for mapping and monitoring geohazards. InSAR techniques excel in determining ground deformation in urban areas, where the coherence of the radar images is high, and the obtainable results are particularly reliable. Thus, measuring urban subsidence has always been one of the main targets of the InSAR analysis. In this paper, we present a brief review on the applications, in the last decades, of both single and multi-interferogram techniques to monitor ground lowering in urban areas along the Italian Peninsula. Because of its geological context, Italy is prone to slow natural subsidence phenomena sometimes aggravated and accelerated, especially along the coasts and in urbanized areas, by anthropogenic factors (i.e., groundwater overexploitation, consolidation in recent urban expansion, geothermal activities). The review will show how the interferometric data allowed the scientific community to increase the knowledge of the phenomena, map their spatial distribution, and reconstruct their temporal evolution. The final goal of the review is to demonstrate the added value of InSAR data in supporting groundwater management and urban development in Italy.

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

Automatic registration of aerial wide-area motion imagery is required to correct the camera parameters in order to eliminate the geocoding errors arising from frequent reinstallation of the camera array on the carrier aircraft. Approaches developed to date solely rely on the information present in the imagery not using a priori knowledge about the environment and the features present in it in the sequence analysis. To this end, we propose a novel method based on dynamic feature extraction and automatic multiscale feature matching to produce per-frame camera pose corrections. The features are extracted from the imagery using one of the three dedicated classifiers and are then robustly matched to the features projected from the datum using a coarse-to-fine multiscale approach. Finally, the bias between the estimated and the actual camera pose is estimated using ordinary least squares optimization performed on the distances between the approved match candidate pairs. The application of the proposed method to 50 frames of very high-resolution aerial imagery captured over mixed terrain at an altitude of 5.3 km demonstrates significant reduction in position error of the features (from 47.76 to 12.31 m) and proves the attractiveness of the approach as an alternative to manual labeling methods using ground control points. [ABSTRACT FROM AUTHOR]

Abstract The study of urban subsidence with multi-temporal SAR interferometry is nowadays a well-consolidated approach. Thanks to this powerful technique, it is possible to detect and to measure ground deformation velocity and time series of displacement with high accuracy. This work focuses the analysis on the subsidence phenomenon that is threating the city of Guzman (Jalisco state, Mexico) by means of multi-temporal SAR interferometry applied to a stack of COSMO-SkyMed data, from 2011 to 2015, and a stack of Sentinel-1 TOPSAR mode images, from 2016 to 2018. The work is intended to carry on the study performed with ENVISAT images covering the time span between 2003 and 2010, allowing the continuous monitoring of the deformation process. [ABSTRACT FROM AUTHOR]

In a previous work, we proposed a PSI-based 4D change detection to detect disappearing and emerging PS points (3D) along with their occurrence dates (1D). Such change points are usually caused by anthropic events, e.g., building constructions in cities. This method first divides an entire SAR image stack into several subsets by a set of break dates. The PS points, which are selected based on their temporal coherences before or after a break date, are regarded as change candidates. Change points are then extracted from these candidates according to their change indices, which are modelled from their temporal coherences of divided image subsets. Finally, we check the evolution of the change indices for each change point to detect the break date that this change occurred. The experiment validated both feasibility and applicability of our method. However, two questions still remain. First, selection of temporal coherence threshold associates with a trade-off between quality and quantity of PS points. This selection is also crucial for the amount of change points in a more complex way. Second, heuristic selection of change index thresholds brings vulnerability and causes loss of change points. In this study, we adapt our approach to identify change points based on statistical characteristics of change indices rather than thresholding. The experiment validates this adaptive approach and shows increase of change points compared with the old version. In addition, we also explore and discuss optimal selection of temporal coherence threshold. [ABSTRACT FROM AUTHOR]

This work focuses on the study of land subsidence processes by means of multi-temporal and multi-frequency InSAR techniques. Specifically, we retrieve the long-term evolution (2003−2018) of the creeping phenomenon producing ground fissuring in the Ciudad Guzmán (Jalisco state, Mexico) urban area. The city is located on the northern side of the Volcan de Colima area, one of the most active Mexican volcanoes. On September 21 2012, Ciudad Guzmán was struck by ground fissures of about 1.5 km of length, causing the deformation of the roads and the propagation of fissures in adjacent buildings. The field surveys showed that fissures follow the escarpments produced during the central Mexico September 19 1985 Mw 8.1 earthquake. We extended the SAR (Synthetic Aperture Radar) interferometric monitoring starting with the multi-temporal analysis of ENVISAT and COSMO-SkyMed datasets, allowing the monitoring of the observed subsidence phenomena affecting the Mexican city. We processed a new stack of Sentinel-1 TOPSAR acquisition mode images along both descending and ascending paths and spanning the 2016−2018 temporal period. The resulting long-term trend observed by satellites, together with data from volcanic bulletin and in situ surveys, seems to suggest that the subsidence is due to the exploitation of the aquifers and that the spatial arrangement of ground deformation is controlled by the position of buried faults.

Cities are facing some of the major challenges of our time: global warming, pollution, waste management, energy efficiency. The territory of the Metropolis of Lyon, France, which brings together 59 municipalities, for a total of 1.3 million inhabitants, has launched a smart city policy aimed, among other things, at finding solutions for these issues. The data platform set up in 2013 is one of the cornerstones of this policy. In this context, the Metropolis of Lyon is deploying solutions that will enable, through the collection of new data, to implement monitoring and action tools in several fields. As part of a European innovation project called "bIoTope", focused on the development of new services based on the Internet of Things, a multidisciplinary team is implementing a system to mitigate the effects of global warming in the city. Thanks to various connected objects allowing a true monitoring of the trees, and by using different data sources, an automatic and intelligent irrigation system is developed. In the field of waste management, several hundred containers in which the inhabitants throw away their used glass for recycling will soon be equipped with fill rate sensors. The main objective is to have this network of sensors interact easily with the container collection trucks. Expected results are an optimization of the collection, thus less fuel consumed, less noise, less traffic jam. The Metropolis of Lyon also participates in the "Smarter Together" project, focused on the development of intelligent duplicable solutions for cities, in the field of energy. A digital tool for analysing consumption and energy production at the level of a neighbourhood is currently being developed. This requires both interfaces with multiple partners, the development of a data model reflecting the reality of the terrain, from the sensors to the buildings, and the implementation of a visualization tool. [ABSTRACT FROM AUTHOR]

The concept of urban sustainability and liveability closely depends on multi-level approaches to environmental issues. The ultimate goal in the field of noise management is to involve citizens and facilitate their participation in urban environmental decisions. The SENSEable Pisa project, based on the concept of Real-Time City and Smart City, presents an acoustic urban monitoring system based on a low-cost data acquisition method for a pervasive outdoor noise measurement. The system is based on the use of noise sensors located on private houses in the center of Pisa, which provide a good model for the current acoustic climate of Mediterranean city centers. In this study, SENSEable acquisitions show a strong anthropogenic component not revealed by public strategic maps. The anthropogenic component, commonly known as movida, becomes increasingly critical in Mediterranean cities, therefore, it is necessary to explore methods highlighting this new source and to adopt strategies for the creation of reliable noise pollution maps. [ABSTRACT FROM AUTHOR]

Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring. [ABSTRACT FROM AUTHOR]

In this letter, we face the problem of multilabeling unmanned aerial vehicle (UAV) imagery, typically characterized by a high level of information content, by proposing a novel method based on convolutional neural networks. These are exploited as a means to yield a powerful description of the query image, which is analyzed after subdividing it into a grid of tiles. The multilabel classification task of each tile is performed by the combination of a radial basis function neural network and a multilabeling layer (ML) composed of customized thresholding operations. Experiments conducted on two different UAV image data sets demonstrate the promising capability of the proposed method compared to the state of the art, at the expense of a higher but still contained computation time. [ABSTRACT FROM PUBLISHER]

Maps are widely used to provide urban managers with information on critical urban issues such as deprivation, unemployment, and segregation. Although administrative boundaries have always played an important role in map making, they are not meaningful for revealing the spatial dynamics of urban phenomena that vary within wards, cross ward boundaries and do not necessarily stop at the city boundary.Recently, very detailed (spatial) data have become available providing opportunities for new types of urban mapping. To process these data into meaningful maps, three aspects are important. First, information on maps should be produced at a spatial scale that is relevant for a particular urban phenomenon. Second, to reveal and monitor urban dynamics, maps of a phenomenon at different moments in time are needed. Finally, to accommodate access to these maps for potential users without (much) expertise in mapping, they should be provided through an easy to use tool.The Regional Monitor Amsterdam (RMA), an online GIS application, deals with these aspects. The purposes of this paper are to explain the mapping methodology adopted in the RMA and to illustrate the usefulness of the tool in urban management. This methodology goes beyond administrative mapping areas with fixed boundaries by introducing ‘data-driven dynamic geographies’. We argue that this methodology produces relevant information by recognizing the scale at which urban phenomena occur. The monitoring tool assists in answering policy questions by easy access to relevant maps for different moments in time.

Persistent Scatterer Interferometry (PS-InSAR) has become an indispensable tool for monitoring surface motion in urban environments. The interferometric configuration of PS-InSAR tends to mix topographic and deformation components in differential interferometric observations. When the upcoming constellation missions such as, e.g., TanDEM-L or TWIN-L provide new standard operating modes, bi-static stacks for deformation monitoring will be more commonly available in the near future. In this paper, we present an analysis of the applicability of such data sets for urban monitoring, using a stack of pursuit monostatic data obtained during the scientific testing phase of the TanDEM-X (TDX) mission. These stacks are characterized by extremely short temporal baselines between the TerraSAR-X (TSX) and TanDEM-X acquisitions at the same interval. We evaluate the advantages of this acquisition mode for urban deformation monitoring with several of the available acquisition pairs. Our proposed method exploits the special properties of this data using a modified processing chain based on the standard PS-InSAR deformation monitoring procedure. We test our approach with a TSX/TDX mono-static pursuit stack over Guangzhou, using both the proposed method and the standard deformation monitoring procedure, and compare the two results. The performance of topographic and deformation estimation is improved by using the proposed processing method, especially regarding high-rise buildings, given the quantitative statistic on temporal coherence, detectable numbers, as well as the PS point density of persistent scatters points, among which the persistent scatter numbers increased by 107.2% and the detectable height span increased by 78% over the standard processing results.

Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds.

Urban sprawl is one of the dominant types of urban development in the world. Although outer growth started from the outset of cities, urban researchers, planners and policy makers are highly concerned about its current extent. Recent development of the Baltic cities and especially trends of their suburban growth have been analysed only partly, because of the relative novelty of the phenomenon and well-established dominance of western cities in the field. This paper attempts to fill this gap and presents a research on conditions and consequences of extensive development of Lithuanian cities. Evidences from the recent growth of the Baltic cities show that decline and sprawl take place simultaneously in major urban regions with possible long-term consequences on their spatial structure. Therefore, this article advocates a need to revise urban policy in the Baltic countries and promote coordinated development of urban and suburban areas in the context of prevailing negative demographic trends and limited capacity of central and local governments to interfere in urban development processes.

Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multitemporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring. [ABSTRACT FROM AUTHOR]